{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Functions\n",
    "\n",
    "The OpenAI compatbile web server in `llama-cpp-python` supports function calling.\n",
    "\n",
    "Function calling allows API clients to specify a schema that gives the model a format it should respond in.\n",
    "Function calling in `llama-cpp-python` works by combining models pretrained for function calling such as [`functionary`](https://huggingface.co/meetkai) with constrained sampling to produce a response that is compatible with the schema.\n",
    "\n",
    "Note however that this improves but does not guarantee that the response will be compatible with the schema.\n",
    "\n",
    "## Requirements\n",
    "\n",
    "Before we begin you will need the following:\n",
    "\n",
    "- A running `llama-cpp-python` server with a function calling compatible model. [See here](https://llama-cpp-python.readthedocs.io/en/latest/server/#function-calling)\n",
    "- The OpenAI Python Client `pip install openai`\n",
    "- (Optional) The Instructor Python Library `pip install instructor`\n",
    "\n",
    "## Function Calling with OpenAI Python Client\n",
    "\n",
    "We'll start with a basic demo that only uses the OpenAI Python Client."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ChatCompletion(id='chatcmpl-a2d9eb9f-7354-472f-b6ad-4d7a807729a3', choices=[Choice(finish_reason='stop', index=0, message=ChatCompletionMessage(content='The current weather in San Francisco is **72°F** (22°C).\\n ', role='assistant', function_call=None, tool_calls=None))], created=1699638365, model='gpt-3.5-turbo-1106', object='chat.completion', system_fingerprint=None, usage=CompletionUsage(completion_tokens=22, prompt_tokens=136, total_tokens=158))\n"
     ]
    }
   ],
   "source": [
    "import openai\n",
    "import json\n",
    "\n",
    "\n",
    "client = openai.OpenAI(\n",
    "    api_key=\"sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\",  # can be anything\n",
    "    base_url=\"http://100.64.159.73:8000/v1\",  # NOTE: Replace with IP address and port of your llama-cpp-python server\n",
    ")\n",
    "\n",
    "\n",
    "# Example dummy function hard coded to return the same weather\n",
    "# In production, this could be your backend API or an external API\n",
    "def get_current_weather(location, unit=\"fahrenheit\"):\n",
    "    \"\"\"Get the current weather in a given location\"\"\"\n",
    "    if \"tokyo\" in location.lower():\n",
    "        return json.dumps({\"location\": \"Tokyo\", \"temperature\": \"10\", \"unit\": \"celsius\"})\n",
    "    elif \"san francisco\" in location.lower():\n",
    "        return json.dumps(\n",
    "            {\"location\": \"San Francisco\", \"temperature\": \"72\", \"unit\": \"fahrenheit\"}\n",
    "        )\n",
    "    elif \"paris\" in location.lower():\n",
    "        return json.dumps({\"location\": \"Paris\", \"temperature\": \"22\", \"unit\": \"celsius\"})\n",
    "    else:\n",
    "        return json.dumps({\"location\": location, \"temperature\": \"unknown\"})\n",
    "\n",
    "\n",
    "def run_conversation():\n",
    "    # Step 1: send the conversation and available functions to the model\n",
    "    messages = [\n",
    "        {\n",
    "            \"role\": \"user\",\n",
    "            \"content\": \"What's the weather like in San Francisco, Tokyo, and Paris?\",\n",
    "        }\n",
    "    ]\n",
    "    tools = [\n",
    "        {\n",
    "            \"type\": \"function\",\n",
    "            \"function\": {\n",
    "                \"name\": \"get_current_weather\",\n",
    "                \"description\": \"Get the current weather in a given location\",\n",
    "                \"parameters\": {\n",
    "                    \"type\": \"object\",\n",
    "                    \"properties\": {\n",
    "                        \"location\": {\n",
    "                            \"type\": \"string\",\n",
    "                            \"description\": \"The city and state, e.g. San Francisco, CA\",\n",
    "                        },\n",
    "                        \"unit\": {\"type\": \"string\", \"enum\": [\"celsius\", \"fahrenheit\"]},\n",
    "                    },\n",
    "                    \"required\": [\"location\"],\n",
    "                },\n",
    "            },\n",
    "        }\n",
    "    ]\n",
    "    response = client.chat.completions.create(\n",
    "        model=\"gpt-3.5-turbo-1106\",\n",
    "        messages=messages,\n",
    "        tools=tools,\n",
    "        tool_choice=\"auto\",  # auto is default, but we'll be explicit\n",
    "    )\n",
    "    response_message = response.choices[0].message\n",
    "    tool_calls = response_message.tool_calls\n",
    "    # Step 2: check if the model wanted to call a function\n",
    "    if tool_calls:\n",
    "        # Step 3: call the function\n",
    "        # Note: the JSON response may not always be valid; be sure to handle errors\n",
    "        available_functions = {\n",
    "            \"get_current_weather\": get_current_weather,\n",
    "        }  # only one function in this example, but you can have multiple\n",
    "        messages.append(response_message)  # extend conversation with assistant's reply\n",
    "        # Step 4: send the info for each function call and function response to the model\n",
    "        for tool_call in tool_calls:\n",
    "            function_name = tool_call.function.name\n",
    "            function_to_call = available_functions[function_name]\n",
    "            function_args = json.loads(tool_call.function.arguments)\n",
    "            function_response = function_to_call(\n",
    "                location=function_args.get(\"location\"),\n",
    "                unit=function_args.get(\"unit\"),\n",
    "            )\n",
    "            messages.append(\n",
    "                {\n",
    "                    \"tool_call_id\": tool_call.id,\n",
    "                    \"role\": \"tool\",\n",
    "                    \"name\": function_name,\n",
    "                    \"content\": function_response,\n",
    "                }\n",
    "            )  # extend conversation with function response\n",
    "        second_response = client.chat.completions.create(\n",
    "            model=\"gpt-3.5-turbo-1106\",\n",
    "            messages=messages,\n",
    "        )  # get a new response from the model where it can see the function response\n",
    "        return second_response\n",
    "\n",
    "\n",
    "print(run_conversation())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Function Calling with Instructor\n",
    "\n",
    "The above example is a bit verbose and requires you to manually verify the schema.\n",
    "\n",
    "For our next examples we'll use the `instructor` library to simplify the process and accomplish a number of different tasks with function calling.\n",
    "\n",
    "You'll first need to install the [`instructor`](https://github.com/jxnl/instructor/).\n",
    "\n",
    "You can do so by running the following command in your terminal:\n",
    "\n",
    "```bash\n",
    "pip install instructor\n",
    "```\n",
    "\n",
    "Below we'll go through a few basic examples taken directly from the [instructor cookbook](https://jxnl.github.io/instructor/)\n",
    "\n",
    "## Basic Usage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name='Jason' age=25\n"
     ]
    }
   ],
   "source": [
    "import instructor\n",
    "from pydantic import BaseModel\n",
    "\n",
    "# Enables `response_model`\n",
    "client = instructor.patch(client=client)\n",
    "\n",
    "\n",
    "class UserDetail(BaseModel):\n",
    "    name: str\n",
    "    age: int\n",
    "\n",
    "\n",
    "user = client.chat.completions.create(\n",
    "    model=\"gpt-3.5-turbo\",\n",
    "    response_model=UserDetail,\n",
    "    messages=[\n",
    "        {\"role\": \"user\", \"content\": \"Extract Jason is 25 years old\"},\n",
    "    ],\n",
    ")\n",
    "\n",
    "assert isinstance(user, UserDetail)\n",
    "assert user.name == \"Jason\"\n",
    "assert user.age == 25\n",
    "\n",
    "print(user)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Text Classification\n",
    "\n",
    "### Single-Label Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "class_label=<Labels.SPAM: 'spam'>\n"
     ]
    }
   ],
   "source": [
    "import enum\n",
    "\n",
    "\n",
    "class Labels(str, enum.Enum):\n",
    "    \"\"\"Enumeration for single-label text classification.\"\"\"\n",
    "\n",
    "    SPAM = \"spam\"\n",
    "    NOT_SPAM = \"not_spam\"\n",
    "\n",
    "\n",
    "class SinglePrediction(BaseModel):\n",
    "    \"\"\"\n",
    "    Class for a single class label prediction.\n",
    "    \"\"\"\n",
    "\n",
    "    class_label: Labels\n",
    "\n",
    "\n",
    "def classify(data: str) -> SinglePrediction:\n",
    "    \"\"\"Perform single-label classification on the input text.\"\"\"\n",
    "    return client.chat.completions.create(\n",
    "        model=\"gpt-3.5-turbo-0613\",\n",
    "        response_model=SinglePrediction,\n",
    "        messages=[\n",
    "            {\n",
    "                \"role\": \"user\",\n",
    "                \"content\": f\"Classify the following text: {data}\",\n",
    "            },\n",
    "        ],\n",
    "    )  # type: ignore\n",
    "\n",
    "\n",
    "prediction = classify(\"Hello there I'm a Nigerian prince and I want to give you money\")\n",
    "assert prediction.class_label == Labels.SPAM\n",
    "print(prediction)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multi-Label Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "class_labels=[<MultiLabels.TECH_ISSUE: 'tech_issue'>, <MultiLabels.BILLING: 'billing'>]\n"
     ]
    }
   ],
   "source": [
    "from typing import List\n",
    "\n",
    "\n",
    "# Define Enum class for multiple labels\n",
    "class MultiLabels(str, enum.Enum):\n",
    "    TECH_ISSUE = \"tech_issue\"\n",
    "    BILLING = \"billing\"\n",
    "    GENERAL_QUERY = \"general_query\"\n",
    "\n",
    "\n",
    "# Define the multi-class prediction model\n",
    "class MultiClassPrediction(BaseModel):\n",
    "    \"\"\"\n",
    "    Class for a multi-class label prediction.\n",
    "    \"\"\"\n",
    "\n",
    "    class_labels: List[MultiLabels]\n",
    "\n",
    "\n",
    "def multi_classify(data: str) -> MultiClassPrediction:\n",
    "    \"\"\"Perform multi-label classification on the input text.\"\"\"\n",
    "    return client.chat.completions.create(\n",
    "        model=\"gpt-3.5-turbo-0613\",\n",
    "        response_model=MultiClassPrediction,\n",
    "        messages=[\n",
    "            {\n",
    "                \"role\": \"user\",\n",
    "                \"content\": f\"Classify the following support ticket: {data}\",\n",
    "            },\n",
    "        ],\n",
    "    )  # type: ignore\n",
    "\n",
    "\n",
    "# Test multi-label classification\n",
    "ticket = \"My account is locked and I can't access my billing info.\"\n",
    "prediction = multi_classify(ticket)\n",
    "print(prediction)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Self-Critique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "question='What is the meaning of life?' answer='According to the Devil, the meaning of life is to live a life of sin and debauchery.'\n",
      "1 validation error for QuestionAnswerNoEvil\n",
      "answer\n",
      "  Assertion failed, The statement promotes sin and debauchery, which can be considered objectionable. [type=assertion_error, input_value='According to the Devil, ... of sin and debauchery.', input_type=str]\n",
      "    For further information visit https://errors.pydantic.dev/2.3/v/assertion_error\n"
     ]
    }
   ],
   "source": [
    "from typing_extensions import Annotated\n",
    "from pydantic import BaseModel, BeforeValidator\n",
    "\n",
    "from instructor import llm_validator\n",
    "\n",
    "\n",
    "question = \"What is the meaning of life?\"\n",
    "context = \"The according to the devil the meaning of live is to live a life of sin and debauchery.\"\n",
    "\n",
    "\n",
    "class QuestionAnswer(BaseModel):\n",
    "    question: str\n",
    "    answer: str\n",
    "\n",
    "\n",
    "qa: QuestionAnswer = client.chat.completions.create(\n",
    "    model=\"gpt-3.5-turbo\",\n",
    "    response_model=QuestionAnswer,\n",
    "    messages=[\n",
    "        {\n",
    "            \"role\": \"system\",\n",
    "            \"content\": \"You are a system that answers questions based on the context. answer exactly what the question asks using the context.\",\n",
    "        },\n",
    "        {\n",
    "            \"role\": \"user\",\n",
    "            \"content\": f\"using the context: {context}\\n\\nAnswer the following question: {question}\",\n",
    "        },\n",
    "    ],\n",
    ")\n",
    "print(qa)\n",
    "\n",
    "\n",
    "class QuestionAnswerNoEvil(BaseModel):\n",
    "    question: str\n",
    "    answer: Annotated[\n",
    "        str,\n",
    "        BeforeValidator(\n",
    "            llm_validator(\"don't say objectionable things\", allow_override=True)\n",
    "        ),\n",
    "    ]\n",
    "\n",
    "\n",
    "try:\n",
    "    qa: QuestionAnswerNoEvil = client.chat.completions.create(\n",
    "        model=\"gpt-3.5-turbo\",\n",
    "        response_model=QuestionAnswerNoEvil,\n",
    "        messages=[\n",
    "            {\n",
    "                \"role\": \"system\",\n",
    "                \"content\": \"You are a system that answers questions based on the context. answer exactly what the question asks using the context.\",\n",
    "            },\n",
    "            {\n",
    "                \"role\": \"user\",\n",
    "                \"content\": f\"using the context: {context}\\n\\nAnswer the following question: {question}\",\n",
    "            },\n",
    "        ],\n",
    "    )\n",
    "except Exception as e:\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Answering Questions with Validated Citations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "question='What did the author do during college?' answer=[Fact(fact='The author, Jason Liu, studied Computational Mathematics and Physics in university.', substring_quote=['Computational Mathematics'])]\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "from typing import List\n",
    "\n",
    "from pydantic import Field, BaseModel, model_validator, FieldValidationInfo\n",
    "\n",
    "\n",
    "class Fact(BaseModel):\n",
    "    fact: str = Field(...)\n",
    "    substring_quote: List[str] = Field(...)\n",
    "\n",
    "    @model_validator(mode=\"after\")\n",
    "    def validate_sources(self, info: FieldValidationInfo) -> \"Fact\":\n",
    "        text_chunks = info.context.get(\"text_chunk\", None)\n",
    "        spans = list(self.get_spans(text_chunks))\n",
    "        self.substring_quote = [text_chunks[span[0] : span[1]] for span in spans]\n",
    "        return self\n",
    "\n",
    "    def get_spans(self, context):\n",
    "        for quote in self.substring_quote:\n",
    "            yield from self._get_span(quote, context)\n",
    "\n",
    "    def _get_span(self, quote, context):\n",
    "        for match in re.finditer(re.escape(quote), context):\n",
    "            yield match.span()\n",
    "\n",
    "\n",
    "class QuestionAnswer(BaseModel):\n",
    "    question: str = Field(...)\n",
    "    answer: List[Fact] = Field(...)\n",
    "\n",
    "    @model_validator(mode=\"after\")\n",
    "    def validate_sources(self) -> \"QuestionAnswer\":\n",
    "        self.answer = [fact for fact in self.answer if len(fact.substring_quote) > 0]\n",
    "        return self\n",
    "\n",
    "\n",
    "def ask_ai(question: str, context: str) -> QuestionAnswer:\n",
    "    return client.chat.completions.create(\n",
    "        model=\"gpt-3.5-turbo-0613\",\n",
    "        temperature=0.0,\n",
    "        response_model=QuestionAnswer,\n",
    "        messages=[\n",
    "            {\n",
    "                \"role\": \"system\",\n",
    "                \"content\": \"You are a world class algorithm to answer questions with correct and exact citations.\",\n",
    "            },\n",
    "            {\"role\": \"user\", \"content\": f\"{context}\"},\n",
    "            {\"role\": \"user\", \"content\": f\"Question: {question}\"},\n",
    "        ],\n",
    "        validation_context={\"text_chunk\": context},\n",
    "    )\n",
    "\n",
    "\n",
    "question = \"What did the author do during college?\"\n",
    "context = \"\"\"\n",
    "My name is Jason Liu, and I grew up in Toronto Canada but I was born in China.\n",
    "I went to an arts high school but in university I studied Computational Mathematics and physics.\n",
    "As part of coop I worked at many companies including Stitchfix, Facebook.\n",
    "I also started the Data Science club at the University of Waterloo and I was the president of the club for 2 years.\n",
    "\"\"\"\n",
    "\n",
    "qa = ask_ai(question, context)\n",
    "print(qa)"
   ]
  }
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